Martin Simonovsky

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 426 researchers and is trusted by 131 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 57 citations into it; #3,140 of 21,575 by trust here, #3,747 by citations
• Artificial Intelligence — 43 citations into it; #4,764 of 35,345 by trust here, #11,072 by citations
• Graph Learning — 21 citations into it; #49 of 693 by trust here, #132 by citations
• Computational Mechanics — 19 citations into it; #448 of 10,001 by trust here, #535 by citations
• Geology — 17 citations into it; #97 of 687 by trust here, #189 by citations
• Computational Theory and Mathematics — 15 citations into it
• Materials Chemistry — 15 citations into it
• Model Architectures — 15 citations into it

Whom Martin Simonovsky trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Geoffrey E. Hinton
3. Yujia Li
4. Sergey Ioffe
5. Alex Krizhevsky
6. Yoshua Bengio
7. Diederik P. Kingma
8. Yann LeCun
9. Christian Szegedy
10. Jimmy Ba

Who trusts Martin Simonovsky overall

1. William L. Hamilton
2. Jiaxuan You
3. Davide Bacciu
4. Federico Errica
5. Rex Ying
6. Dinh Van Tran
7. Nicoló Navarin
8. Matthias Fey
9. Nicola De Cao
10. Yan Zhang

Papers from Martin Simonovsky's perspective

Ordered by how strongly the researchers Martin Simonovsky trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders (2018) — 1,007 citations
• Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (2017) — 1,361 citations
• Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs (2017) — 1,463 citations